The invention relates to signal-processing systems. More specifically, the invention relates to audio encoders.
The use of digital audio has become widespread in audio and audio-visual systems. Therefore, the demand for more effective and efficient digital audio systems has increased, so that the same memory can be used to store more audio files. Further, an efficient digital audio system enables the same bandwidth to be used for transferring additional audio files. Therefore, system designers, as well as manufacturers, are striving to improve audio data-compression systems.
In conventional systems, perceptive encoding is mostly used for compression of audio signals. In any given situation, the human ear is capable of hearing only certain frequencies within the audible frequency band. This is taken into account in a psycho-acoustic model. This model takes the effects of simultaneous and temporal masking into account to define a masking threshold at different frequency levels. The masking threshold is defined as the minimum level of the particular frequency at which the human ear can hear. Therefore, the model helps an encoder to improve data compression by defining the frequencies that will not be heard by the human ear, so that the encoder can ignore these frequencies during bit allocation.
In a conventional encoder, an inner iteration loop or a rate control loop is carried out. In this loop, the quantization step is varied to match the number of bits available with the demand for bits generated by the coding employed. If the number of bits required by the frequencies selected by the psycho-acoustic model is more than the number of bits available, the quantization step is varied.
Further, the frequency spectrum of the input signal is divided into a number of frequency bands, and a scale factor is calculated for each of the frequency bands. Scale factors are calculated to shape the quantization noise according to the masking threshold. If the quantization noise of any band is above the masking threshold, the scale factor is adjusted to reduce the quantization noise. This iterative process of selecting the scale factors is known as the outer iteration loop or the distortion control loop.
An encoder generally performs various calculations, including the calculation of scale factors. However, the known methods for calculating scale factors are complex and computationally inefficient, which make the overall encoding process time-consuming.
Thus, there is a need for a computationally efficient method for calculation of scale factors.